SCH: INT: Novel Techniques for Patient-centric Disease Management using Automatically Inferred Behavioral Biomarkers and Sensor-Supported Contextual Self-Report

以患者为中心的、个性化的、精准的医疗和健康的愿景，只有当一个个体？我们的自我护理和临床决策是由一个丰富的、可预测的个体模型提供的吗？S健康状态。移动技术的发展和传播为高度详细和个性化的数据收集创造了前所未有的机会，这种收集方式更加细致、不引人注目，甚至可以负担得起；这些数据包括活动水平、位置模式、睡眠、消费、交流和社会互动。然而，将这种潜力转化为实践需要我们开发算法和方法，将这些原始数据转化为可操作的信息。该研究将开发新颖和可推广的技术，以获得与个人健康和临床决策相关的稳健措施。该团队将开发和评估将原始人类活动数据转换为临床可操作的行为生物标志物的工具。这需要创造性地利用基础技术能力（即被动数据捕获、数据分析和机器学习、数据可视化、用户体验），以及对基础健康状况和管理的严格理解（即功能性健康措施、可实现的最佳健康结果、患者在坚持治疗方面面临的挑战、与药物和治疗的其他方面相关的风险和益处，以及临床决策）。该方法广泛适用于疾病管理（例如，自身免疫性疾病、胃肠道疾病、抑郁症、认知衰退和神经系统疾病），但也需要根据具体情况和个体进行调整。因此，我们将在特定的背景下进行这项初步工作，即对三种主要疾病的慢性疼痛管理：类风湿关节炎、骨关节炎和腰痛。与我们最初的目标领域疼痛管理相关的行为生物标志物主要围绕：(1)活动水平下降；（ii）压力增加；（iii）睡眠质量下降；（iv）功能下降，例如出行距离缩短或无法上班。移动电话的被动感应能力在跟踪睡眠、活动水平的变化、压力、社会隔离、地理位置和其他几个可能是疼痛干扰的先决条件或症状的指标方面的有效性已在以前得到证实。虽然行为生物标志物广泛依赖于被动捕获的数据流（如活动、位置、通信、应用程序使用和音频），但在一些重要的情况下，需要自我报告数据来补充或澄清被动收集的数据。然而，由于长度、问题设计或两者兼而有之，评估相关症状和行为的标准化患者调查工具不适合日常使用。此外，传统形式的自我报告往往是侵入性的，繁重的，并且有很高的损耗率。一种新的方法，上下文回忆，旨在通过三个关键机制来缓解与自我报告相关的问题：优化提示的传递，为用户提供关键的上下文线索以提高回忆，并采用视觉输入技术作为长格式测量的替代方案，而长格式测量不能很好地扩展到频繁的移动自我报告。个性化疾病管理的方法在可负担性和可及性方面有意可扩展。被动的数据收集不需要用户的注意，上下文回忆是一种自我报告的形式，专为忙碌的个人设计，他们有一系列的需求和时间限制，以及潜在的读写和计算限制。这种方法面向临床医生的部分也被设计用于资源有限的临床环境中，临床医生在特殊的时间压力下工作。该团队将从通常服务不足的社区招募患者和临床医生参与参与式设计过程。这项工作的总体贡献将包括开发和评估：(1)软件技术，将被动监测和自我报告的数据流组合和转换为临床有意义的、可操作的和个性化的指标，我们称之为行为生物标志物；(2)情境回忆，允许收集高粒度和情境特定的自我报告数据，从患者的角度增强被动捕获的数据，同时平衡在平衡回忆偏差和可用性方面面临的紧张；(3)将与临床领域专家的合作系统化，以开发并将行为生物标志物整合到特定疾病的临床决策中。我们将创建并评估一个模块化和可扩展的分析和用户交互技术套件，旨在促进迭代实现和评估。这些模块本身将是一种贡献，但同样重要的是对行为生物标志物作为精准医学驱动因素的整体方法的评估。

The vision of patient-centric, personalized, precision medicine and wellness will be fully realized only when an individual?s self-care and clinical decision making are informed by a rich, predictive model of that individual?s health status. The evolution and dissemination of mobile technology has created unprecedented opportunities for highly detailed and personalized data collection in a far more granular, unobtrusive, and even affordable way; these data include activity levels, location patterns, sleep, consumption, and communication and social interaction. However, turning this potential into practice requires that we develop the algorithms and methodologies to transform these raw data into actionable information. The research will develop novel and generalizable techniques to derive robust measures relevant to individual health and clinical decision making. The team will develop and evaluate tools that convert raw human-activity data into clinically actionable behavioral biomarkers. This demands creative uses of the underlying technical capabilities (i.e., passive data capture, data analysis and machine learning, data visualization, user experience), as well as rigorous understanding of the underlying health condition and management (i.e. functional health measures, achievable and optimal health outcomes, patient challenges in adherence, risks and benefits associated with medication and other aspects of treatment, and clinical decision making). The approach has broad applicability across disease management (e.g., auto-immune, gastrointestinal, depression, cognitive decline, and neurologic disorders), but also calls for tailoring to specific conditions and individuals. Therefore, we will conduct this initial work in a specific context, that of chronic pain management for three prominent conditions: rheumatoid arthritis, osteoarthritis, and lower back pain. The behavioral biomarkers associated with our initial target domain, pain management, center around: (i) decline in activity levels; (ii) increase in stress; (iii) decrease in sleep quality; (iv) drop in function, e.g., reduction in travel distance or inability to go to work. The effectiveness of passive sensing capabilities of the mobile phone to track sleep, changes in activity level, stress, social isolation, geographic location and several other indicators that are likely antecedents or symptoms of pain interference has been demonstrated previously. While behavioral biomarkers rely extensively on passively captured data streams (such as activity, location, communication, application usage and audio), there remain important cases in which self-report data is required to augment or clarify passively collected data. However, the standardized patient survey instruments that assess relevant symptoms and behavior are not suitable for use on a daily basis because of length, question design, or both. Further, traditional forms of self report are often intrusive, burdensome, and suffer high rates of attrition. A new approach, contextual recall, aims to mitigate the issues related to self-report through three key mechanisms: optimizing the delivery of prompts, providing the user with key contextual cues to improve recall, and employing visual input techniques as an alternative to long-form measures that do not scale well to frequent mobile self-reports. The approach to personalizing disease management is intentionally scalable in terms of affordability and accessibility. Passive data collection requires no user attention, and contextual recall is a form of self-report designed for busy individuals with a range of demands and constraints on their time, as well as potential literacy and numeracy constraints. The clinician-facing components of this approach are also designed to work in resource-constrained clinical settings where clinicians are under particular time pressure. The team will recruit patients and clinicians from typically underserved communities to engage in the participatory design process. The overall contributions of this work will include development and evaluation of: (1) software techniques to combine and transform passively monitored and self-reported data streams into clinically meaningful, actionable, and personalized indicators, which we call behavioral biomarkers; (2) contextual recall that allows the collection of highly granular and contextually specific self-report data to enhance passively captured data with information from the patient perspective, while balancing the tension faced in balancing recall bias and usability; and (3) a methodology that systematizes the collaboration with clinical domain experts to develop and integrate behavioral biomarkers into clinical decision making for specific diseases. We will create and evaluate a modular and extensible suite of analytics and user interaction techniques designed to facilitate iterative implementation and evaluation. These modules will themselves be a contribution, but equally important will be the evaluation of the overall approach of behavioral biomarkers as a driver of precision medicine.